Emergency Landing Field Identification Based on a Hierarchical Ensemble Transfer Learning Model
Andreas Klos, Marius Rosenbaum, Wolfram H. Schiffmann · 2020
The full loss of thrust of an aircraft requires fast and reliable decisions of the pilot. If no published landing field is within reach, an emergency landing field must be selected. The choice of a suitable emergency landing field denotes a crucial task to avoid unnecessary damage of the aircraft, risk for the civil population as well as the crew and all passengers on board. Especially in case of instrument meteorological conditions it is indispensable to use a database of suitable emergency landing fields. Thus, based on public available digital orthographic photos and digital surface models, we created various datasets with different sample sizes to facilitate training and testing of neural networks. Each dataset consists of a set of data layers. The best compositions of these data layers as well as the best performing transfer learning models are selected. The hyperparameters of the chosen models for each sample size are optimized with Bayesian and Bandit optimization. The models outputs were investigated with respect to the input data by the utilization of layer-wise relevance propagation. With optimized models we created an ensemble model to improve the segmentation performance. Finally, an area around the airport of Arnsberg in North Rhine-Westphalia was segmented into 26.252 km2as landable and 221.329 km2as not suitable for an emergency landing. In sum 54,997 emergency landing fields are identified and stored in a database. The verification of the final approach's obstacle clearance is left unconsidered.